Molecular Determinants of Calcitriol Signaling and Sensitivity in Glioma Stem-like Cells
Bibliographic record
Abstract
Abstract Glioblastoma is the most common primary brain cancer in adults and represents one of the worst cancer diagnosis for the patients. Suffering from a poor prognosis and limited treatment options, tumor recurrences are virtually inevitable. Additionally, treatment resistance is very common for this disease and worsens the prognosis. These and others factors are hypothesized to be largely due to the fact that glioblastoma cells are known to be able to obtain stem-like traits and thereby driving these phenotypes. Recently, we could show that the in vitro and ex vivo treatment of glioblastoma stem-like cells with the hormonally active form of Vitamin D3, Calcitriol (1α,25(OH)2-vitamin D3) can block stemness in a subset of cell lines and reduce tumor growth. Here, we expanded our cell panel to over 40 different cultures and can show that, while half of the tested cell lines are sensitive, a quarter can be classified as high-responders. Using genetic and proteomic analysis, we further determined that treatment success can be partially explained by specific polymorphism of the Vitamin D3 receptor and that high-responders display a proteome suggestive of blockade of stemness, as well as migratory potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".